Evaluation of a chronic disease management system for the treatment and management of diabetes in primary health care practices in Ontario: an observational study.
Bibliographic record
Abstract
BACKGROUND: Computerized chronic disease management systems (CDMSs), when aligned with clinical practice guidelines, have the potential to effectively impact diabetes care. OBJECTIVE: The objective was to measure the difference between optimal diabetes care and actual diabetes care before and after the introduction of a computerized CDMS. METHODS: This 1-year, prospective, observational, pre/post study evaluated the use of a CDMS with a diabetes patient registry and tracker in family practices using patient enrolment models. Aggregate practice-level data from all rostered diabetes patients were analyzed. The primary outcome measure was the change in proportion of patients with up-to-date "ABC" monitoring frequency (i.e., hemoglobin A1c, blood pressure, and cholesterol). Changes in the frequency of other practice care and treatment elements (e.g., retinopathy screening) were also determined. Usability and satisfaction with the CDMS were measured. RESULTS: Nine sites, 38 health care providers, and 2,320 diabetes patients were included. The proportion of patients with up-to-date ABC (12%), hemoglobin A1c (45%), and cholesterol (38%) monitoring did not change over the duration of the study. The proportion of patients with up-to-date blood pressure monitoring improved, from 16% to 20%. Data on foot examinations, retinopathy screening, use of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, and documentation of self-management goals were not available or not up to date at baseline for 98% of patients. By the end of the study, attitudes of health care providers were more negative on the Training, Usefulness, Daily Practice, and Support from the Service Provider domains of the CDMS, but more positive on the Learning, Using, Practice Planning, CDMS, and Satisfaction domains. LIMITATIONS: Few practitioners used the CDMS, so it was difficult to draw conclusions about its efficacy. Simply giving health care providers a potentially useful technology will not ensure its use. CONCLUSIONS: This real-world evaluation of a web-based CDMS for diabetes failed to impact physician practice due to limited use of the system. PLAIN LANGUAGE SUMMARY: Patients and health care providers need timely access to information to ensure proper diabetes care. This study looked at whether a computer-based system at the doctor's office could improve diabetes management. However, few clinics and health care providers used the system, so no improvement in diabetes care was seen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".